Medical images, such as three-dimensional CT or MRI images, must sometimes be segmented. Segmentation of an image refers to the process of defining or reconstructing different internal structures, as well as a patient outline, in an image. The structures, or “Regions of Interest” (ROIs), could be for example specific internal organs identifiable in the images. Segmented ROIs are often represented as solid or translucent objects in the three-dimensional images so as to be viewable, and possibly also manipulatable, for a user. Segmentation of medical images is usually required in the field of radiotherapy treatment planning since target volumes (e.g. tumors) and “Organs at Risk” (OARs) must be defined and outlined in the images in order to facilitate planning of a treatment using a Treatment Planning System (TPS).
Structures can be manually segmented in the images using various tools, such as tools for drawing contours in CT slices. However, such manual segmentation is cumbersome and time-consuming. Especially in the field of adaptive radiotherapy, where the shape and/or position of structures may have to be adjusted during the course of the treatment, the segmentation of ROIs might constitute a major part of a treatment planning process. Therefore, many automatic or semi-automatic methods for segmenting ROIs in medical images have been proposed. Some of these are based on the use of templates or “atlases” comprising medical images with already segmented structures (e.g. internal organs). The structures are transferred into the new, and not yet segmented, medical image of a subject (hereinafter denoted “patient image” in contrast to an “atlas image”) and adapted to the new geometry. These methods are often referred to as “atlas-based segmentation”. An advantage of such methods is that the work previously done segmenting the atlas image is re-utilized when segmenting the patient image.
The results of a segmentation using atlas-based segmentation depend on the similarity between the used atlas image and the patient image. Obviously, also other parameters, such as the image registration algorithm used, are relevant. In general, though, it is hard to obtain a satisfactory result if the geometry of the atlas image differs too much from the geometry of the patient image. Various approaches have been suggested to address the problem of geometrical differences between an atlas image and a patient image.
One approach is to register multiple different atlases with the patient image and select the one that yields the best match for segmentation. For example, all available atlases may be deformably registered with the patient image and the atlas of which the registration indicates the highest degree of similarity (e.g. the highest correlation coefficient, mutual information value etc.) is selected and used for segmenting ROIs in the patient image by employing the deformable registration.
Deformably registering each atlas with the patient image is a very time-consuming process requiring substantial computer processing power. Furthermore, the use of a single atlas for segmenting all regions of interest in an image might not always be optimal.
Another approach is to use the results from all of the atlas registrations and separately propagate all variants of the structures into the patient image and create averaged structures from the plurality of segmentations. When combining the segmentations, different segmentations are sometimes weighted differently based on various criteria. However, also such methods require that each atlas is deformably registered with the patient image.
A somewhat different approach is to create an average atlas of a plurality of atlases and deformably register the average atlas with the patient image. No selection of the most similar atlas is done, and this approach will often yield unsatisfactory results since the atlas will not be patient specific.
Furthermore, in many atlas-based segmentation methods previously used, substantial atlas pre-processing is required. For example, depending on the method used, a large number of landmarks must often be defined in the atlases.
An aim of the present invention is to overcome or at least mitigate the problems above and achieve atlas-based segmentation which is both accurate and fast, i.e. computationally cheap, and provide for effective selection amongst a large set of atlases.